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ChromSwitch_OCRA_v2.r
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###### Chromswitch part 2 - using chromhmm states and different query ######
library(chromswitch)
## prepare states as GRanges list
hmm.states <- dir("~/Documents/OCRA_ChromHMM_hg19/")
hmm.states <- dir("~/Documents/OCRA_Files/ChromHMM_Edited/")
## Consensus states
ft.cons <- fread("~/Documents/OCRA_Files/Consensus_states/FT_consensus_UCSC.bed", skip = 1)
hg.cons <- fread("~/Documents/OCRA_Files/Consensus_states/HGSOC_consensus_UCSC.bed", skip = 1)
iose.cons <- fread("~/Documents/OCRA_Files/Consensus_states/IOSE_consensus_UCSC.bed", skip = 1)
ft.cons <- ft.cons[, 1:4]
colnames(ft.cons) <- c("chr", "start", "end", "state_n")
ft.cons <- makeGRangesFromDataFrame(na.omit(ft.cons), keep.extra.columns = T)
hg.cons <- hg.cons[, 1:4]
colnames(hg.cons) <- c("chr", "start", "end", "state_n")
hg.cons <- makeGRangesFromDataFrame(na.omit(hg.cons), keep.extra.columns = T)
iose.cons <- iose.cons[, 1:4]
colnames(iose.cons) <- c("chr", "start", "end", "state_n")
iose.cons <- makeGRangesFromDataFrame(na.omit(iose.cons), keep.extra.columns = T)
cons.states <- GRangesList(ft.cons, hg.cons, iose.cons)
names(cons.states) <- c("FTSEC_Consensus", "HGSOC_Consensus", "iOSE_Consensus")
#aux <- fread(paste0("~/Documents/OCRA_ChromHMM_hg19/", hmm.states[1]), skip = 1)
aux <- fread(paste0("~/Documents/OCRA_Files/ChromHMM_Edited/", hmm.states[1]), skip = 1) ## cleaner chromhmm calls
aux <- aux[, 1:4]
colnames(aux) <- c("chr", "start", "end", "state_n")
list.states <- GRangesList(makeGRangesFromDataFrame(aux, keep.extra.columns = T))
names(list.states) <- hmm.states[1]
w <- which(list.states[[1]]$state_n %in% c("TRS", "CTCF"))
list.states[[1]] <- list.states[[i]][-c(w)]
for( i in 2:length(hmm.states)) {
# aux <- fread(paste0("~/Documents/OCRA_ChromHMM_hg19/", hmm.states[i]), skip = 1)
aux <- fread(paste0("~/Documents/OCRA_Files/ChromHMM_Edited/", hmm.states[i]), skip = 1)
aux <- aux[, 1:4]
colnames(aux) <- c("chr", "start", "end", "state_n")
list.states[[i]] <- makeGRangesFromDataFrame(aux, keep.extra.columns = T)
names(list.states)[i] <- hmm.states[i]
w <- which(list.states[[i]]$state_n %in% c("TRS", "CTCF"))
list.states[[i]] <- list.states[[i]][-c(w)]
}
metadata <- data.frame(Sample = names(list.states), Condition = NA)
metadata$Condition <- c("HGSOC", "Endometrioid", "Muc", "CCOC", rep("FTSEC", 3), "Muc",
"HGSOC", rep("IOSE", 2), "CCOC", "HGSOC", "Muc", "HGSOC", "CCOC", "HGSOC", "LGSOC")
cons.metadata <- data.frame(Sample = names(cons.states), Condition = c("FTSEC", "HGSOC", "IOSE"))
## query will be DEG from ocra_rnaseq.R
deg <- read.table("~/Documents/OCRA_Files/Table_DEG_FTSEC_vs_HGSOC.txt")
library(biomaRt)
ensembl <- useMart("ensembl")
ensembl <- useDataset("hsapiens_gene_ensembl",mart=ensembl)
bm <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'hgnc_symbol'),
filters=c('hgnc_symbol'),
values=deg[deg$STATUS %in% "Down", ]$GeneSymbol,
mart=ensembl)
bm2 <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'ensembl_gene_id','hgnc_symbol'),
filters=c('ensembl_gene_id'),
values= gsub("\\.[0-9]","", deg[deg$STATUS %in% "Up", ] %>% rownames),
mart=ensembl)
bm3 <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'hgnc_symbol'),
filters=c('hgnc_symbol'),
values=deg$GeneSymbol,
mart=ensembl)
genes.ft.reg <- bm
colnames(genes.ft.reg) <- c("chr", "start", "end", "Gene")
genes.ft.reg$chr <- paste0("chr", genes.ft.reg$chr)
genes.ft.reg <- makeGRangesFromDataFrame(genes.ft.reg, keep.extra.columns = T)
genes.hg.reg <- bm2
colnames(genes.hg.reg) <- c("chr", "start", "end", "Gene")
genes.hg.reg$chr <- paste0("chr", genes.hg.reg$chr)
genes.hg.reg <- genes.hg.reg[, -5]
genes.hg.reg <- makeGRangesFromDataFrame(genes.hg.reg, keep.extra.columns = T)
genes.deg.all <- bm3
colnames(genes.deg.all) <- c("chr", "start", "end", "Gene")
genes.deg.all$chr <- paste0("chr", genes.deg.all$chr)
genes.deg.all <- makeGRangesFromDataFrame(genes.deg.all, keep.extra.columns = T)
## HGSOC vs FTSEC (no ft282 and only 2 hgsoc lines) ##
deg.new <- read.table("~/Documents/OCRA_Files/DEG_HGSOC_FTSEC_no282.txt")
deg.new.hg <- deg.new[deg.new$STATUS %in% "Up",]
deg.new.ft <- deg.new[deg.new$STATUS %in% "Down",]
# get gene coordinates from hg19 gtf annotation file
gtf.hg19 <- readGFF("~/reference_files/gencode.v34lift37.annotation.gtf.gz")
gtf.hg19 <- gtf.hg19[gtf.hg19$type %in% "gene",]
gtf.hg19$gene_id <- substr(gtf.hg19$gene_id, 1, 15)
aux <- gtf.hg19[gtf.hg19$gene_id %in% substr(rownames(deg.new.hg),1,15),]
aux <- aux[, c(1,4:5,7,11)]
deg.new.hg.gr <- makeGRangesFromDataFrame(aux, keep.extra.columns = T)
aux <- gtf.hg19[gtf.hg19$gene_id %in% substr(rownames(deg.new.ft),1,15),]
aux <- aux[, c(1,4:5,7,11)]
deg.new.ft.gr <- makeGRangesFromDataFrame(aux, keep.extra.columns = T)
# get gene names and coordnates from the rnaseq object ocra.rnaseq
# ocra.rna[ocra.rna$Geneid %in% rownames(deg.new),] -> aux
# ocra.rna[ocra.rna$Geneid %in% rownames(deg.new.hg),] -> aux
#
# aux <- aux[, c(2:5)]
# aux$gene <- rownames(aux)
# deg.new.gr <- makeGRangesFromDataFrame(aux, keep.extra.columns = T)
# deg.new.hg.gr <- makeGRangesFromDataFrame(aux, keep.extra.columns = T)
### all genes as quuery ###
gtf.hg19 <- readGFF("~/reference_files/gencode.v34lift37.annotation.gtf.gz")
gtf.hg19 <- gtf.hg19[gtf.hg19$type %in% "gene" , ]
gtf.hg19 <- gtf.hg19[gtf.hg19$gene_type %in% c("protein_coding", "lncRNA", "miRNA"),]
query.all.genes <- gtf.hg19[, c(1,4:5,7,11)]
colnames(query.all.genes) <- c("chr","start", "end", "strand")
query.all.genes <- makeGRangesFromDataFrame(query.all.genes, keep.extra.columns = T)
library(BiocParallel)
register(default = T, BPPARAM = DoparParam()) ## to avoid error in parallel processing
# ft genes
out.summ <- callSummary(query = genes.ft.reg, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
out.summ.cons <- callSummary(query = genes.deg.all, # Input 1: Query regions
# , # Input 2: Metadata
metadata = cons.metadata,
# metadata = cons.metadata[cons.metadata$Condition %in% c("FTSEC", "HGSOC"),],
# # Input 3: Peaks
# peaks = cons.states[cons.metadata$Sample[cons.metadata$Condition %in% c("FTSEC", "HGSOC")]],
peaks = cons.states,
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = F)
# hg genes
out.summ2 <- callSummary(query = genes.hg.reg, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
out.summ.all <- callSummary(query = deg.new.gr, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),][c(2,4,6,8),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]][c(2,4,6,8),],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = F)
#break down new deg by hg and ft genes
out.summ.new.hg <- callSummary(query = deg.new.hg.gr, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),][c(2,4,6,8),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]][c(2,4,6,8),],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = F)
write.table(out.summ.new.hg, quote = F, row.names = F, sep = "\t", file = '~/Documents/OCRA_Files/ChromSwitch_DEG_HGSOC_new.txt')
out.summ.new.ft <- callSummary(query = deg.new.ft.gr, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),][c(2,4,6,8),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]][c(2,4,6,8),],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = F)
write.table(out.summ.new.ft, quote = F, row.names = F, sep = "\t", file = '~/Documents/OCRA_Files/ChromSwitch_DEG_FTSEC_new.txt')
out.bin.all <- callBinary(query = deg.new.gr, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),][c(2,4,6,8),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]][c(2,4,6,8),]
)
write.table(out.bin.all, quote = F, sep = "\t",
row.names = F, file = "~/Documents/OCRA_Files/ChromSwitch_newDEG_FT_HG_binary.txt")
out.summ.all <- callSummary(query = query.all.genes, # Input 1: Query regions
# metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata
metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),][c(2,4,6,8),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]][c(2,4,6,8),],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
write.table(out.summ, quote = F, row.names = F, file = '~/Documents/OCRA_Files/ChromSwitch_DEG_FT_Summary.txt')
write.table(out.summ2, quote = F, row.names = F, file = '~/Documents/OCRA_Files/ChromSwitch_DEG_HG_Summary.txt')
write.table(out.summ3, quote = F, row.names = F, file = '~/Documents/OCRA_Files/ChromSwitch_DEG_All_Summary.txt')
out.summ.all <- callSummary(query = query.all.genes, # Input 1: Query regions
metadata = metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),], # Input 2: Metadata dataframe
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]], # Input 3: Peaks
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F)
gr.all.switch <- out.summ.all
gr.all.switch <- separate(gr.all.switch, "query", into = c("start", "end"), sep = "-", remove = F)
gr.all.switch <- separate(gr.all.switch, "start", into = c('chr', 'start'), sep = ':', remove = T)
gr.all.switch <- gr.all.switch[, -1]
gr.all.switch <- makeGRangesFromDataFrame(gr.all.switch, keep.extra.columns = T)
###now annotate which switch with a gene near by
grch37 = useMart(biomart="ENSEMBL_MART_ENSEMBL",
host="grch37.ensembl.org", path="/biomart/martservice", dataset="hsapiens_gene_ensembl")
genes_all <- getBM(
attributes=c("hgnc_symbol","entrezgene_id","chromosome_name","start_position","end_position"),
mart = mart)
colnames(genes_all)[3:5] <- c("chr", "start", "end")
genes_all$chr <- paste0("chr", genes_all$chr)
genes_all <- makeGRangesFromDataFrame(genes_all, keep.extra.columns = T)
genes_all <- genes_all[seqnames(genes_all) %in% seqnames(gr.all.switch)]
gr.all.switch <- gr.all.switch[seqnames(gr.all.switch) %in% seqnames(genes_all)]
#ovp <- findOverlaps(gr.all.switch, genes(TxDb.Hsapiens.UCSC.hg19.knownGene))
ovp <- findOverlaps(gr.all.switch, genes_all)
gr.all.switch <- gr.all.switch[queryHits(ovp)]
#gr.all.switch$Gene <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)$gene_id[subjectHits(ovp)]
gr.all.switch$Gene <- genes_all$hgnc_symbol[subjectHits(ovp)]
## filter by Consensus / cluster quality
gr.all.switch.filter <- gr.all.switch[gr.all.switch$Consensus >= 0.75, ]
all.switch.filter <- as.data.frame(gr.all.switch.filter)
all.switch.filter <- all.switch.filter[order(all.switch.filter$Consensus, decreasing = T), ]
#gr.all.switch.filter <- gr.all.switch.filter[sort(gr.all.switch.filter$Consensus, decreasing = T)]
write.table(all.switch.filter, quote =F, row.names = F, sep = "\t", file = "~/Documents/OCRA_Files/ChromSwitch_All_Genes_Query.txt")
out.summ2 <- callSummary(query = genes.hg.reg , # Input 1: Query regions
metadata = metadata[c(1,5,7,9,13,15,17),], # Input 2: Metadata dataframe
peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F)
a <- callBinary(query = genes.ft.reg, # Input 1: Query regions
metadata =metadata[metadata$Condition %in% c("FTSEC", "HGSOC"),], # Input 2: Metadata dataframe
peaks = list.states[metadata$Sample[metadata$Condition %in% c("FTSEC", "HGSOC")]],
heatmap =F) # Input 3: Peaks
write.table(a, quote = F, row.names = F, file = '~/Documents/OCRA_Files/ChromSwitch_DEG_FT_binary.txt')
## MUC
bm3 <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'hgnc_symbol'),
filters=c('hgnc_symbol'),
values=c(genes.muc,genes.muc.hg),
mart=ensembl)
genes.muc.reg <- bm3
colnames(genes.muc.reg) <- c("chr", "start", "end", "Gene")
genes.muc.reg$chr <- paste0("chr", genes.muc.reg$chr)
genes.muc.reg <- makeGRangesFromDataFrame(genes.muc.reg, keep.extra.columns = T)
out.summ.muc <- callSummary(query = genes.muc.reg, # Input 1: Query regions
metadata[metadata$Condition %in% c("Muc", "HGSOC"),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("Muc", "HGSOC")]],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
##### iOSE
deg <- read.table("~/Documents/OCRA_Files/Table_DEG_OSEC_vs_HGSOC.txt")
bm4 <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'ensembl_gene_id','hgnc_symbol'),
filters=c('ensembl_gene_id'),
values=gsub("\\.[0-9]","",rownames(deg)),
mart=ensembl)
genes.iose.reg <- bm4
colnames(genes.iose.reg) <- c("chr", "start", "end", "GeneID", "GeneSymbol")
genes.iose.reg$chr <- paste0("chr", genes.iose.reg$chr)
genes.iose.reg <- makeGRangesFromDataFrame(genes.iose.reg, keep.extra.columns = T)
out.summ.ose <- callSummary(query = genes.iose.reg, # Input 1: Query regions
metadata[metadata$Condition %in% c("IOSE", "HGSOC"),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("IOSE", "HGSOC")]],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
write.table(out.summ.ose, quote = F, row.names = F, file = '~/Documents/OCRA_Files/ChromSwitch_DEG_iOSE_HGSOC_Summary.txt')
##### CCOC
bm5 <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'hgnc_symbol'),
filters=c('hgnc_symbol'),
values=genes.ccoc,
mart=ensembl)
genes.ccoc.reg <- bm5
colnames(genes.ccoc.reg) <- c("chr", "start", "end", "Gene")
genes.ccoc.reg$chr <- paste0("chr", genes.ccoc.reg$chr)
genes.ccoc.reg <- makeGRangesFromDataFrame(genes.ccoc.reg, keep.extra.columns = T)
out.summ.ccoc <- callSummary(query = genes.ccoc.reg, # Input 1: Query regions
metadata[metadata$Condition %in% c("CCOC", "Endometrioid"),],
# peaks = list.states[metadata$Sample[c(1,5,7,9,13,15,17)],], # Input 3: Peaks
peaks = list.states[metadata$Sample[metadata$Condition %in% c("CCOC", "Endometrioid")]],
mark = "Chromatin States", # Arbitrary string describing the data type
heatmap = F,
normalize = TRUE)
###